Passive techniques of steropsis involve triangulation of features viewed from different positions or at different times, under ambient lighting conditions, as described in “Structure From Stereo-A Review,” Dhond, Umesh R, and Aggarwal, J. K., IEEE Transactions On Systems, Man, And Cybernetics, Vol. 19, No, 6, November/December 1989. The major steps in stereopsis are preprocessing, matching, and recovering depth information. As described in the reference, the process of matching features between multiple images is perhaps the most critical stage of stereopsis. This step is also called the correspondence problem.
It is also well known that stereo matching using edge segments, rather than individual points, provides increased immunity from the effects of isolated points, and provides an additional disambiguating constraint in matching segments of different stereoscopic images taken of the same scene. A variety of algorithms can be used for matching edge segments that meet criteria for 3-D segments occurring along a smooth surface. In addition, a trinocular camera arrangement provides further information that can improve a binocular depth map with points (or edges) matched if they satisfy additional geometric constraints, such as length and orientation.
Once the segmented points have been identified and the depth information recovered, the 3-D object structure can be obtained which can then be used in 3-D object recognition. The purpose of this embodiment is more to segment the 3-D scene into 3-D objects that are spatially separated in a 2-D plane, rather than object recognition. Therefore, an elaborate 3-D object re-construction is not necessary.
However, the prior combinations of feature detection, matching, 3-D segmentation are computationally intensive, either decreasing speed or increasing cost of automated systems. Furthermore, prior methods lack robustness because of susceptibility to noise and confusion among match candidates. 3-D data is mostly used for object recognition, as opposed to segmentation of objects placed in a plane in 3-D space. Known techniques, typically using 2D segmentation, assume a fixed relationship between the camera system and the plane under consideration, that is, they do not facilitate specifying any arbitrary plane.